The influence of inertia weight on the Particle Swarm Optimization algorithm

• Autorzy: Cekus D. , Skrobek D.

Identyfikatory

DOI

10.17512/jamcm.2018.4.01

• Języki publikacji: EN

Abstrakty

EN

The paper presents the use of the Particle Swarm Optimization (PSO) algorithm to find the shortest trajectory connecting two defined points while avoiding obstacles. The influence of the inertia weight and the number of population adopted in the first iteration of the PSO algorithm was examined for the length of the sought trajectory. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is widely used in literature.

Słowa kluczowe

EN

particle swarm optimization (PSO); PSO algorithm; inertia weight; trajectory

PL

optymalizacja rojem cząstek; PSO; algorytm PSO; metoda PSO; algorytm optymalizacji rojem cząstek; trajektoria; współczynnik wagowy

• Wydawca: Wydawnictwo Politechniki Częstochowskiej
• Czasopismo: Journal of Applied Mathematics and Computational Mechanics
• Rocznik: 2018
• Tom: Vol. 17, nr 4
• Strony: 5--11
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Cekus D.

• Institute of Mechanics and Machine Design Fundamentals Czestochowa University of Technology Częstochowa, Poland

autor

Skrobek D.

• Institute of Mechanics and Machine Design Fundamentals Czestochowa University of Technology Częstochowa, Poland

Bibliografia

• [1] Kennedy, J., & Eberhart, R.C. (1995). Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neutral Networks, Volume 4, 1942-1948.
• [2] Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and Information Science, 3, 1, 180-184.
• [3] Tarnowski, W. (2011). Optymalizacja i polioptymalizacja w technice. Koszalin: Wydawnictwo Uczelniane Politechniki Koszalińskiej.
• [4] Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Transaction on Evolutionary Computation, 6, 2, 58-73.
• [5] Cheng, R., & Yao, M. (2001). Particle Swarm Optimizer with time-varying parameters based on a Novel Operator. Applied Mathematics & Information Sciences, 5, 2, 33-38.
• [6] Szczepanik, M. (2013). Algorytmy rojowe w optymalizacji układów mechanicznych. Gliwice: Wydawnictwo Politechniki Śląskiej.
• [7] Cekus, D., & Skrobek, D. (2016). Trajectory optimization of a SCARA manipulator using Particle Swarm Optimization. Machine Dynamics Research, 40, 1, 45-52.
• [8] Cekus, D., & Waryś, P. (2015). Identification of parameters of discrete-continuous models. AIP Conf. Proc. 1648, 850055, DOI: 10.1063/1.4913110.
• [9] Skrobek, D., & Cekus, D. (2019). Optimization of the operation of the anthropomorphic manipulator in a three-dimensional working space. Engineering Optimization, DOI: 10.1080/0305215X.2018.1564919
• [10] Ao, Y., & Chi, H. (2010). An adaptive differential evolution to solve constrained optimization problem in engineering design. Engineering, 2, 65-77.
• [11] Lin, W., Lee, W., & Hong, T. (2003). Adapting crossover and mutation rates in genetic algorithms. Journal of information science and Engineering, 19, 889-903.
• [12] Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., & Abraham, A. (2011). Interia weight strategies in particle swarm optimization. Third World Congress on Nature and Biologically Inspired Computing (NaBIC), IEEE, 640-647, DOI: 10.1109/NaBIC.2011.6089659.
• [13] Ting, T.O., Shi, Y., Cheng, S., & Lee, S. (2012). Exponential Inertia Weight for Particle Swarm Optimization. Advances in Swarm Intelligence, vol. 7331, Berlin, Heidelberg: Springer, DOI: 10.1007/978-3-642-30976-2_10.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).